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import torch

torch.manual_seed(0)

# Positive test
torch.set_printoptions(precision=8)
activation = torch.nn.Softplus(beta=5.0, threshold=2.5)
x = torch.rand(1, 2, 3, 4, requires_grad=True)
print("Input: ", x)
output = activation(x)
print("Output: ", output)
output.sum().backward()
print("Gradient for input: ", x.grad)

# Negative test
torch.set_printoptions(precision=8)
activation = torch.nn.Softplus(beta=-5.0, threshold=-2.5)
x = torch.rand(1, 2, 3, 4, requires_grad=True)
print("Input: ", x)
output = activation(x)
print("Output: ", output)
output.sum().backward()
print("Gradient for input: ", x.grad)
